Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations96
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.0 KiB
Average record size in memory160.0 B

Variable types

Numeric17
Categorical2

Alerts

subtokenization_indicator_min has constant value "1.0" Constant
GOODS_DESCRIPTION_len_chars_max is highly overall correlated with GOODS_DESCRIPTION_len_chars_mean and 11 other fieldsHigh correlation
GOODS_DESCRIPTION_len_chars_mean is highly overall correlated with GOODS_DESCRIPTION_len_chars_max and 12 other fieldsHigh correlation
GOODS_DESCRIPTION_len_chars_median is highly overall correlated with GOODS_DESCRIPTION_len_chars_mean and 7 other fieldsHigh correlation
GOODS_DESCRIPTION_len_chars_min is highly overall correlated with GOODS_DESCRIPTION_len_chars_max and 7 other fieldsHigh correlation
GOODS_DESCRIPTION_len_chars_std is highly overall correlated with GOODS_DESCRIPTION_len_chars_max and 4 other fieldsHigh correlation
GOODS_DESCRIPTION_len_chars_sum is highly overall correlated with GOODS_DESCRIPTION_len_chars_max and 10 other fieldsHigh correlation
GOODS_DESCRIPTION_len_words_max is highly overall correlated with GOODS_DESCRIPTION_len_chars_max and 11 other fieldsHigh correlation
GOODS_DESCRIPTION_len_words_mean is highly overall correlated with GOODS_DESCRIPTION_len_chars_max and 11 other fieldsHigh correlation
GOODS_DESCRIPTION_len_words_median is highly overall correlated with GOODS_DESCRIPTION_len_chars_max and 9 other fieldsHigh correlation
GOODS_DESCRIPTION_len_words_min is highly overall correlated with GOODS_DESCRIPTION_len_chars_minHigh correlation
GOODS_DESCRIPTION_len_words_std is highly overall correlated with GOODS_DESCRIPTION_len_chars_max and 4 other fieldsHigh correlation
GOODS_DESCRIPTION_len_words_sum is highly overall correlated with GOODS_DESCRIPTION_len_chars_max and 10 other fieldsHigh correlation
HS06_count is highly overall correlated with GOODS_DESCRIPTION_len_chars_max and 9 other fieldsHigh correlation
subtokenization_indicator_max is highly overall correlated with GOODS_DESCRIPTION_len_chars_max and 12 other fieldsHigh correlation
subtokenization_indicator_mean is highly overall correlated with GOODS_DESCRIPTION_len_chars_mean and 4 other fieldsHigh correlation
subtokenization_indicator_median is highly overall correlated with subtokenization_indicator_mean and 1 other fieldsHigh correlation
subtokenization_indicator_std is highly overall correlated with subtokenization_indicator_max and 2 other fieldsHigh correlation
subtokenization_indicator_sum is highly overall correlated with GOODS_DESCRIPTION_len_chars_max and 10 other fieldsHigh correlation
GOODS_DESCRIPTION_len_words_min is highly imbalanced (79.9%) Imbalance
GOODS_DESCRIPTION_len_words_sum has unique values Unique
GOODS_DESCRIPTION_len_words_std has unique values Unique
GOODS_DESCRIPTION_len_chars_mean has unique values Unique
GOODS_DESCRIPTION_len_chars_std has unique values Unique
subtokenization_indicator_sum has unique values Unique
subtokenization_indicator_mean has unique values Unique
subtokenization_indicator_std has unique values Unique

Reproduction

Analysis started2025-05-15 18:03:21.035223
Analysis finished2025-05-15 18:05:11.917202
Duration1 minute and 50.88 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

HS06_count
Real number (ℝ)

High correlation 

Distinct89
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2789.375
Minimum5
Maximum54901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:12.250712image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile14
Q1121.25
median527.5
Q32109.25
95-th percentile11762.75
Maximum54901
Range54896
Interquartile range (IQR)1988

Descriptive statistics

Standard deviation7357.814
Coefficient of variation (CV)2.6378002
Kurtosis30.409234
Mean2789.375
Median Absolute Deviation (MAD)507
Skewness5.1538992
Sum267780
Variance54137426
MonotonicityNot monotonic
2025-05-15T15:05:12.805448image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
317 3
 
3.1%
11 2
 
2.1%
293 2
 
2.1%
15 2
 
2.1%
19 2
 
2.1%
468 2
 
2.1%
3793 1
 
1.0%
2422 1
 
1.0%
1533 1
 
1.0%
233 1
 
1.0%
Other values (79) 79
82.3%
ValueCountFrequency (%)
5 1
1.0%
6 1
1.0%
10 1
1.0%
11 2
2.1%
15 2
2.1%
19 2
2.1%
22 1
1.0%
27 1
1.0%
32 1
1.0%
34 1
1.0%
ValueCountFrequency (%)
54901 1
1.0%
33571 1
1.0%
28476 1
1.0%
16173 1
1.0%
12218 1
1.0%
11611 1
1.0%
7972 1
1.0%
7921 1
1.0%
7526 1
1.0%
4285 1
1.0%

GOODS_DESCRIPTION_len_words_sum
Real number (ℝ)

High correlation  Unique 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12799.042
Minimum15
Maximum256770
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:13.524588image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile34.5
Q1608.5
median2135.5
Q39261.75
95-th percentile53337.75
Maximum256770
Range256755
Interquartile range (IQR)8653.25

Descriptive statistics

Standard deviation34999.098
Coefficient of variation (CV)2.7345092
Kurtosis29.491323
Mean12799.042
Median Absolute Deviation (MAD)2052.5
Skewness5.1288628
Sum1228708
Variance1.2249368 × 109
MonotonicityNot monotonic
2025-05-15T15:05:14.283655image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
775 1
 
1.0%
1558 1
 
1.0%
1270 1
 
1.0%
12460 1
 
1.0%
11014 1
 
1.0%
6693 1
 
1.0%
779 1
 
1.0%
641 1
 
1.0%
2591 1
 
1.0%
7700 1
 
1.0%
Other values (86) 86
89.6%
ValueCountFrequency (%)
15 1
1.0%
20 1
1.0%
23 1
1.0%
27 1
1.0%
30 1
1.0%
36 1
1.0%
46 1
1.0%
52 1
1.0%
81 1
1.0%
85 1
1.0%
ValueCountFrequency (%)
256770 1
1.0%
157199 1
1.0%
150753 1
1.0%
73862 1
1.0%
53412 1
1.0%
53313 1
1.0%
36418 1
1.0%
33864 1
1.0%
33169 1
1.0%
18492 1
1.0%

GOODS_DESCRIPTION_len_words_min
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
1
93 
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters96
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 93
96.9%
2 3
 
3.1%

Length

2025-05-15T15:05:15.032746image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T15:05:15.439968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 93
96.9%
2 3
 
3.1%

Most occurring characters

ValueCountFrequency (%)
1 93
96.9%
2 3
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 93
96.9%
2 3
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 93
96.9%
2 3
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 93
96.9%
2 3
 
3.1%

GOODS_DESCRIPTION_len_words_mean
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1185112
Minimum2.0909091
Maximum6.4683544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:15.938849image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2.0909091
5-th percentile2.8858234
Q13.7481697
median4.1198804
Q34.5974651
95-th percentile5.2906959
Maximum6.4683544
Range4.3774453
Interquartile range (IQR)0.8492954

Descriptive statistics

Standard deviation0.76212921
Coefficient of variation (CV)0.18504969
Kurtosis0.72674148
Mean4.1185112
Median Absolute Deviation (MAD)0.47154938
Skewness-0.041199556
Sum395.37708
Variance0.58084093
MonotonicityNot monotonic
2025-05-15T15:05:16.483432image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 2
 
2.1%
6.007751938 1
 
1.0%
3.845768521 1
 
1.0%
4.613106257 1
 
1.0%
4.54748142 1
 
1.0%
4.365949119 1
 
1.0%
3.343347639 1
 
1.0%
4.056962025 1
 
1.0%
3.760522496 1
 
1.0%
4.05904059 1
 
1.0%
Other values (85) 85
88.5%
ValueCountFrequency (%)
2.090909091 1
1.0%
2.4 1
1.0%
2.454545455 1
1.0%
2.589905363 1
1.0%
2.736842105 1
1.0%
2.935483871 1
1.0%
3 2
2.1%
3.066666667 1
1.0%
3.079545455 1
1.0%
3.109090909 1
1.0%
ValueCountFrequency (%)
6.46835443 1
1.0%
6.007751938 1
1.0%
5.550689376 1
1.0%
5.317406143 1
1.0%
5.294037084 1
1.0%
5.289582107 1
1.0%
5.250712251 1
1.0%
5.1125 1
1.0%
5.071669794 1
1.0%
5.071428571 1
1.0%

GOODS_DESCRIPTION_len_words_median
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.578125
Minimum2
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:17.106257image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum6
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.77909571
Coefficient of variation (CV)0.21773854
Kurtosis0.29014474
Mean3.578125
Median Absolute Deviation (MAD)0
Skewness-0.044005382
Sum343.5
Variance0.60699013
MonotonicityNot monotonic
2025-05-15T15:05:17.559360image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 49
51.0%
3 30
31.2%
2 7
 
7.3%
5 6
 
6.2%
2.5 3
 
3.1%
6 1
 
1.0%
ValueCountFrequency (%)
2 7
 
7.3%
2.5 3
 
3.1%
3 30
31.2%
4 49
51.0%
5 6
 
6.2%
6 1
 
1.0%
ValueCountFrequency (%)
6 1
 
1.0%
5 6
 
6.2%
4 49
51.0%
3 30
31.2%
2.5 3
 
3.1%
2 7
 
7.3%

GOODS_DESCRIPTION_len_words_max
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.447917
Minimum3
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:18.081302image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5.75
Q113
median17.5
Q324
95-th percentile32.25
Maximum41
Range38
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.9204862
Coefficient of variation (CV)0.42934312
Kurtosis-0.057594053
Mean18.447917
Median Absolute Deviation (MAD)5.5
Skewness0.37294986
Sum1771
Variance62.734101
MonotonicityNot monotonic
2025-05-15T15:05:18.563491image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
14 8
 
8.3%
23 6
 
6.2%
20 5
 
5.2%
13 5
 
5.2%
17 5
 
5.2%
15 5
 
5.2%
21 5
 
5.2%
12 4
 
4.2%
25 4
 
4.2%
16 4
 
4.2%
Other values (23) 45
46.9%
ValueCountFrequency (%)
3 1
 
1.0%
4 1
 
1.0%
5 3
3.1%
6 1
 
1.0%
7 2
2.1%
8 1
 
1.0%
9 3
3.1%
10 3
3.1%
11 2
2.1%
12 4
4.2%
ValueCountFrequency (%)
41 1
 
1.0%
37 2
2.1%
34 1
 
1.0%
33 1
 
1.0%
32 1
 
1.0%
31 1
 
1.0%
29 1
 
1.0%
28 3
3.1%
27 3
3.1%
26 4
4.2%

GOODS_DESCRIPTION_len_words_std
Real number (ℝ)

High correlation  Unique 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4903831
Minimum0.53935989
Maximum4.1689802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:19.074527image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.53935989
5-th percentile1.3730419
Q12.1834948
median2.5404806
Q32.9078441
95-th percentile3.5554833
Maximum4.1689802
Range3.6296203
Interquartile range (IQR)0.72434935

Descriptive statistics

Standard deviation0.6249779
Coefficient of variation (CV)0.25095653
Kurtosis0.94486299
Mean2.4903831
Median Absolute Deviation (MAD)0.36738186
Skewness-0.38359552
Sum239.07678
Variance0.39059738
MonotonicityNot monotonic
2025-05-15T15:05:19.688757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.859841569 1
 
1.0%
3.04727659 1
 
1.0%
2.163082001 1
 
1.0%
2.904064587 1
 
1.0%
2.926473309 1
 
1.0%
2.557651519 1
 
1.0%
2.187870352 1
 
1.0%
2.84021097 1
 
1.0%
2.754881031 1
 
1.0%
2.391902154 1
 
1.0%
Other values (86) 86
89.6%
ValueCountFrequency (%)
0.53935989 1
1.0%
1.035725481 1
1.0%
1.055597326 1
1.0%
1.099783528 1
1.0%
1.247219129 1
1.0%
1.414982776 1
1.0%
1.446916463 1
1.0%
1.484174497 1
1.0%
1.531335925 1
1.0%
1.608083757 1
1.0%
ValueCountFrequency (%)
4.168980208 1
1.0%
3.859841569 1
1.0%
3.607145572 1
1.0%
3.575449727 1
1.0%
3.574472297 1
1.0%
3.549153593 1
1.0%
3.461727596 1
1.0%
3.155637188 1
1.0%
3.130722923 1
1.0%
3.130303308 1
1.0%

GOODS_DESCRIPTION_len_chars_sum
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81515.052
Minimum95
Maximum1657412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:20.280777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile204.75
Q14016.5
median14208.5
Q355776.75
95-th percentile343578
Maximum1657412
Range1657317
Interquartile range (IQR)51760.25

Descriptive statistics

Standard deviation226820.03
Coefficient of variation (CV)2.782554
Kurtosis29.502104
Mean81515.052
Median Absolute Deviation (MAD)13616.5
Skewness5.1507801
Sum7825445
Variance5.1447328 × 1010
MonotonicityNot monotonic
2025-05-15T15:05:21.022964image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11397 2
 
2.1%
4417 1
 
1.0%
83074 1
 
1.0%
8432 1
 
1.0%
76719 1
 
1.0%
67240 1
 
1.0%
42107 1
 
1.0%
4977 1
 
1.0%
4261 1
 
1.0%
15320 1
 
1.0%
Other values (85) 85
88.5%
ValueCountFrequency (%)
95 1
1.0%
123 1
1.0%
148 1
1.0%
172 1
1.0%
204 1
1.0%
205 1
1.0%
261 1
1.0%
388 1
1.0%
494 1
1.0%
501 1
1.0%
ValueCountFrequency (%)
1657412 1
1.0%
1023987 1
1.0%
998884 1
1.0%
443120 1
1.0%
364062 1
1.0%
336750 1
1.0%
226624 1
1.0%
205919 1
1.0%
204300 1
1.0%
120224 1
1.0%

GOODS_DESCRIPTION_len_chars_min
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9791667
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:21.528892image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q34
95-th percentile7.25
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5826638
Coefficient of variation (CV)0.39773749
Kurtosis3.2515458
Mean3.9791667
Median Absolute Deviation (MAD)1
Skewness1.661975
Sum382
Variance2.5048246
MonotonicityNot monotonic
2025-05-15T15:05:22.109105image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 34
35.4%
4 32
33.3%
2 9
 
9.4%
5 8
 
8.3%
6 6
 
6.2%
8 2
 
2.1%
7 2
 
2.1%
9 2
 
2.1%
10 1
 
1.0%
ValueCountFrequency (%)
2 9
 
9.4%
3 34
35.4%
4 32
33.3%
5 8
 
8.3%
6 6
 
6.2%
7 2
 
2.1%
8 2
 
2.1%
9 2
 
2.1%
10 1
 
1.0%
ValueCountFrequency (%)
10 1
 
1.0%
9 2
 
2.1%
8 2
 
2.1%
7 2
 
2.1%
6 6
 
6.2%
5 8
 
8.3%
4 32
33.3%
3 34
35.4%
2 9
 
9.4%

GOODS_DESCRIPTION_len_chars_mean
Real number (ℝ)

High correlation  Unique 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.890457
Minimum13.454545
Maximum41.556962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:22.608619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum13.454545
5-th percentile17.946774
Q123.215338
median25.811239
Q328.466038
95-th percentile34.333797
Maximum41.556962
Range28.102417
Interquartile range (IQR)5.2507007

Descriptive statistics

Standard deviation4.8779838
Coefficient of variation (CV)0.18840856
Kurtosis0.8090321
Mean25.890457
Median Absolute Deviation (MAD)2.6208265
Skewness0.096608964
Sum2485.4839
Variance23.794726
MonotonicityNot monotonic
2025-05-15T15:05:23.138731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.24031008 1
 
1.0%
33.64163823 1
 
1.0%
21.51020408 1
 
1.0%
28.40392447 1
 
1.0%
27.76218002 1
 
1.0%
27.46705806 1
 
1.0%
21.36051502 1
 
1.0%
26.96835443 1
 
1.0%
22.23512337 1
 
1.0%
25.2398524 1
 
1.0%
Other values (86) 86
89.6%
ValueCountFrequency (%)
13.45454545 1
1.0%
13.66666667 1
1.0%
15.63636364 1
1.0%
16.5615142 1
1.0%
17.4 1
1.0%
18.12903226 1
1.0%
18.60984848 1
1.0%
19 1
1.0%
19.70909091 1
1.0%
20.3881932 1
1.0%
ValueCountFrequency (%)
41.55696203 1
1.0%
36.00416667 1
1.0%
35.33495539 1
1.0%
35.07810086 1
1.0%
34.61425891 1
1.0%
34.24031008 1
1.0%
33.64163823 1
1.0%
32.58646617 1
1.0%
32.24543849 1
1.0%
31.91067538 1
1.0%

GOODS_DESCRIPTION_len_chars_median
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.463542
Minimum9
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:23.671479image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile14.875
Q120
median22.25
Q325.125
95-th percentile30
Maximum36
Range27
Interquartile range (IQR)5.125

Descriptive statistics

Standard deviation4.5400236
Coefficient of variation (CV)0.20210632
Kurtosis0.65863743
Mean22.463542
Median Absolute Deviation (MAD)2.75
Skewness-0.13618489
Sum2156.5
Variance20.611815
MonotonicityNot monotonic
2025-05-15T15:05:24.172777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
22 12
12.5%
24 10
10.4%
26 7
 
7.3%
21 7
 
7.3%
20 7
 
7.3%
25 7
 
7.3%
27 7
 
7.3%
23 6
 
6.2%
18 5
 
5.2%
17 5
 
5.2%
Other values (16) 23
24.0%
ValueCountFrequency (%)
9 1
 
1.0%
12 1
 
1.0%
13 1
 
1.0%
14 1
 
1.0%
14.5 1
 
1.0%
15 2
 
2.1%
16 1
 
1.0%
17 5
5.2%
18 5
5.2%
19 4
4.2%
ValueCountFrequency (%)
36 1
 
1.0%
32 1
 
1.0%
31 1
 
1.0%
30 3
3.1%
29 1
 
1.0%
28 2
 
2.1%
27 7
7.3%
26 7
7.3%
25.5 1
 
1.0%
25 7
7.3%

GOODS_DESCRIPTION_len_chars_max
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.04167
Minimum24
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:24.672898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile36.75
Q183.25
median101
Q3150
95-th percentile150
Maximum150
Range126
Interquartile range (IQR)66.75

Descriptive statistics

Standard deviation38.430502
Coefficient of variation (CV)0.35570075
Kurtosis-0.96890396
Mean108.04167
Median Absolute Deviation (MAD)36.5
Skewness-0.42343553
Sum10372
Variance1476.9035
MonotonicityNot monotonic
2025-05-15T15:05:25.210389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
150 25
26.0%
100 6
 
6.2%
149 6
 
6.2%
88 3
 
3.1%
113 3
 
3.1%
94 2
 
2.1%
87 2
 
2.1%
99 2
 
2.1%
84 2
 
2.1%
77 2
 
2.1%
Other values (39) 43
44.8%
ValueCountFrequency (%)
24 1
1.0%
30 2
2.1%
31 1
1.0%
33 1
1.0%
38 1
1.0%
42 1
1.0%
43 1
1.0%
54 1
1.0%
55 1
1.0%
56 1
1.0%
ValueCountFrequency (%)
150 25
26.0%
149 6
 
6.2%
144 1
 
1.0%
143 1
 
1.0%
140 1
 
1.0%
135 1
 
1.0%
134 1
 
1.0%
133 1
 
1.0%
132 1
 
1.0%
129 1
 
1.0%

GOODS_DESCRIPTION_len_chars_std
Real number (ℝ)

High correlation  Unique 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.463062
Minimum5.3733348
Maximum24.703237
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:25.784827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum5.3733348
5-th percentile8.7080778
Q112.931953
median15.331074
Q318.055048
95-th percentile21.776572
Maximum24.703237
Range19.329902
Interquartile range (IQR)5.1230948

Descriptive statistics

Standard deviation4.0719599
Coefficient of variation (CV)0.26333464
Kurtosis-0.25337489
Mean15.463062
Median Absolute Deviation (MAD)2.6248741
Skewness-0.10820759
Sum1484.454
Variance16.580858
MonotonicityNot monotonic
2025-05-15T15:05:26.416637image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.96797199 1
 
1.0%
15.8134861 1
 
1.0%
14.28715009 1
 
1.0%
17.72909639 1
 
1.0%
17.25737594 1
 
1.0%
15.50843253 1
 
1.0%
14.71184673 1
 
1.0%
17.93598698 1
 
1.0%
16.23438024 1
 
1.0%
15.03329405 1
 
1.0%
Other values (86) 86
89.6%
ValueCountFrequency (%)
5.373334837 1
1.0%
5.802298395 1
1.0%
7.73489311 1
1.0%
7.823619398 1
1.0%
8.395766129 1
1.0%
8.812181651 1
1.0%
8.875311679 1
1.0%
9.275510106 1
1.0%
9.770037011 1
1.0%
10.19757878 1
1.0%
ValueCountFrequency (%)
24.70323701 1
1.0%
23.95720033 1
1.0%
22.96797199 1
1.0%
22.16979928 1
1.0%
21.823475 1
1.0%
21.76093802 1
1.0%
21.72689805 1
1.0%
21.10993586 1
1.0%
21.00291573 1
1.0%
20.95948574 1
1.0%

subtokenization_indicator_sum
Real number (ℝ)

High correlation  Unique 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5431.5762
Minimum9.4
Maximum108947.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:27.016938image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum9.4
5-th percentile19.829167
Q1178.6354
median944.54691
Q34088.8101
95-th percentile23025.952
Maximum108947.02
Range108937.62
Interquartile range (IQR)3910.1747

Descriptive statistics

Standard deviation14678.45
Coefficient of variation (CV)2.7024291
Kurtosis30.235703
Mean5431.5762
Median Absolute Deviation (MAD)908.71861
Skewness5.16365
Sum521431.31
Variance2.1545689 × 108
MonotonicityNot monotonic
2025-05-15T15:05:27.590638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181.7710127 1
 
1.0%
846.6701687 1
 
1.0%
623.302381 1
 
1.0%
4614.795259 1
 
1.0%
4282.989997 1
 
1.0%
2994.08142 1
 
1.0%
311.386039 1
 
1.0%
250.2694784 1
 
1.0%
1051.298301 1
 
1.0%
3473.194787 1
 
1.0%
Other values (86) 86
89.6%
ValueCountFrequency (%)
9.4 1
1.0%
9.821428571 1
1.0%
16 1
1.0%
16.16666667 1
1.0%
16.66666667 1
1.0%
20.88333333 1
1.0%
24.06666667 1
1.0%
26.79908009 1
1.0%
35.77564103 1
1.0%
35.88095238 1
1.0%
ValueCountFrequency (%)
108947.0216 1
1.0%
67054.18812 1
1.0%
58631.14629 1
1.0%
31715.65547 1
1.0%
23964.395 1
1.0%
22713.13714 1
1.0%
17189.63416 1
1.0%
14955.23916 1
1.0%
12475.90271 1
1.0%
9079.447805 1
1.0%

subtokenization_indicator_min
Categorical

Constant 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
1.0
96 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters288
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 96
100.0%

Length

2025-05-15T15:05:28.057705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T15:05:28.399888image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1.0 96
100.0%

Most occurring characters

ValueCountFrequency (%)
1 96
33.3%
. 96
33.3%
0 96
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 96
33.3%
. 96
33.3%
0 96
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 96
33.3%
. 96
33.3%
0 96
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 96
33.3%
. 96
33.3%
0 96
33.3%

subtokenization_indicator_mean
Real number (ℝ)

High correlation  Unique 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8478004
Minimum1.3064248
Maximum2.8896593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:28.769376image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.3064248
5-th percentile1.4101278
Q11.6112548
median1.7729694
Q32.0119592
95-th percentile2.3864634
Maximum2.8896593
Range1.5832345
Interquartile range (IQR)0.40070441

Descriptive statistics

Standard deviation0.31712636
Coefficient of variation (CV)0.17162371
Kurtosis1.3685078
Mean1.8478004
Median Absolute Deviation (MAD)0.19197106
Skewness0.99435466
Sum177.38884
Variance0.10056913
MonotonicityNot monotonic
2025-05-15T15:05:29.332843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.409077618 1
 
1.0%
2.889659279 1
 
1.0%
1.590057094 1
 
1.0%
1.708550633 1
 
1.0%
1.768369115 1
 
1.0%
1.95308638 1
 
1.0%
1.336420768 1
 
1.0%
1.58398404 1
 
1.0%
1.525832077 1
 
1.0%
1.830888132 1
 
1.0%
Other values (86) 86
89.6%
ValueCountFrequency (%)
1.306424792 1
1.0%
1.336420768 1
1.0%
1.385263685 1
1.0%
1.392222222 1
1.0%
1.409077618 1
1.0%
1.410477899 1
1.0%
1.458142488 1
1.0%
1.46969697 1
1.0%
1.485742862 1
1.0%
1.497962856 1
1.0%
ValueCountFrequency (%)
2.889659279 1
1.0%
2.887057102 1
1.0%
2.642158046 1
1.0%
2.606536797 1
1.0%
2.42121609 1
1.0%
2.374879196 1
1.0%
2.354018098 1
1.0%
2.327005013 1
1.0%
2.286300175 1
1.0%
2.28403324 1
1.0%

subtokenization_indicator_median
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6203099
Minimum1
Maximum2.3333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:29.945579image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2767857
Q11.5
median1.6
Q31.75
95-th percentile2
Maximum2.3333333
Range1.3333333
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.25559668
Coefficient of variation (CV)0.15774555
Kurtosis0.64860803
Mean1.6203099
Median Absolute Deviation (MAD)0.1
Skewness0.20703322
Sum155.54975
Variance0.065329661
MonotonicityNot monotonic
2025-05-15T15:05:30.424335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1.5 27
28.1%
2 12
12.5%
1.666666667 11
11.5%
1.75 8
 
8.3%
1.6 7
 
7.3%
1.333333333 6
 
6.2%
1 3
 
3.1%
1.833333333 3
 
3.1%
1.416666667 2
 
2.1%
1.625 2
 
2.1%
Other values (13) 15
15.6%
ValueCountFrequency (%)
1 3
 
3.1%
1.055555556 1
 
1.0%
1.25 1
 
1.0%
1.285714286 1
 
1.0%
1.333333333 6
 
6.2%
1.4 2
 
2.1%
1.416666667 2
 
2.1%
1.45 1
 
1.0%
1.5 27
28.1%
1.571428571 1
 
1.0%
ValueCountFrequency (%)
2.333333333 1
 
1.0%
2.25 1
 
1.0%
2.2 1
 
1.0%
2 12
12.5%
1.897368421 1
 
1.0%
1.833333333 3
 
3.1%
1.777777778 1
 
1.0%
1.75 8
8.3%
1.714285714 2
 
2.1%
1.666666667 11
11.5%

subtokenization_indicator_max
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.364193
Minimum2
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:31.289395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15.5
median9
Q317
95-th percentile30.5
Maximum59
Range57
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation9.6923248
Coefficient of variation (CV)0.78390276
Kurtosis4.9267632
Mean12.364193
Median Absolute Deviation (MAD)4.8333333
Skewness1.8228954
Sum1186.9625
Variance93.941161
MonotonicityNot monotonic
2025-05-15T15:05:31.730648image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 7
 
7.3%
3 6
 
6.2%
9 6
 
6.2%
4 5
 
5.2%
11 4
 
4.2%
7 3
 
3.1%
10.5 3
 
3.1%
16 3
 
3.1%
25 3
 
3.1%
17 3
 
3.1%
Other values (40) 53
55.2%
ValueCountFrequency (%)
2 1
 
1.0%
2.0625 1
 
1.0%
2.5 2
 
2.1%
3 6
6.2%
3.4 2
 
2.1%
4 5
5.2%
4.333333333 1
 
1.0%
4.4 1
 
1.0%
4.5 1
 
1.0%
4.666666667 1
 
1.0%
ValueCountFrequency (%)
59 1
 
1.0%
38 1
 
1.0%
36 1
 
1.0%
33 1
 
1.0%
32 1
 
1.0%
30 1
 
1.0%
27 1
 
1.0%
26 1
 
1.0%
25 3
3.1%
24 2
2.1%

subtokenization_indicator_std
Real number (ℝ)

High correlation  Unique 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0427778
Minimum0.32026997
Maximum4.756294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-15T15:05:32.198918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.32026997
5-th percentile0.50750871
Q10.69806242
median0.88526552
Q31.1212212
95-th percentile2.1402593
Maximum4.756294
Range4.436024
Interquartile range (IQR)0.4231588

Descriptive statistics

Standard deviation0.68524075
Coefficient of variation (CV)0.65713016
Kurtosis15.993194
Mean1.0427778
Median Absolute Deviation (MAD)0.20596233
Skewness3.6080105
Sum100.10667
Variance0.46955488
MonotonicityNot monotonic
2025-05-15T15:05:32.660646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4513244466 1
 
1.0%
4.75629399 1
 
1.0%
0.7150886604 1
 
1.0%
0.8916367722 1
 
1.0%
0.903294991 1
 
1.0%
1.270069027 1
 
1.0%
0.5005838218 1
 
1.0%
0.6590552354 1
 
1.0%
0.712196267 1
 
1.0%
0.9500163379 1
 
1.0%
Other values (86) 86
89.6%
ValueCountFrequency (%)
0.3202699703 1
1.0%
0.4014293246 1
1.0%
0.4513244466 1
1.0%
0.4798989793 1
1.0%
0.5005838218 1
1.0%
0.5098170071 1
1.0%
0.5297515291 1
1.0%
0.5630678951 1
1.0%
0.5822853742 1
1.0%
0.5870198238 1
1.0%
ValueCountFrequency (%)
4.75629399 1
1.0%
4.596942019 1
1.0%
3.042963297 1
1.0%
2.370812634 1
1.0%
2.321687516 1
1.0%
2.079783195 1
1.0%
1.99371846 1
1.0%
1.684930545 1
1.0%
1.491320463 1
1.0%
1.461329544 1
1.0%

Interactions

2025-05-15T15:05:04.582042image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:21.959322image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:27.903769image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:34.224336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:40.673766image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:47.556084image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:54.233133image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:00.525602image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:07.183332image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:13.784245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:20.150120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:25.988142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:32.620609image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:39.505396image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:45.574996image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:52.155835image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:58.358233image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:05:04.963639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:22.355347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-05-15T15:05:02.662205image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:05:08.484306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:26.600569image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:32.683352image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:38.958112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:46.057529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:52.670336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:58.972345image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:05.702844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:12.169582image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:18.651364image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:24.433788image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:31.041960image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:37.894900image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:44.261013image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:50.528656image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:57.039556image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:05:02.996711image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:05:08.793699image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:26.961509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:33.080971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:39.487033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:46.488607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:53.016671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:59.448646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:06.047981image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:12.844134image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:19.102510image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:24.815122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:31.562598image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:38.282938image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:44.568877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:50.957144image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:57.399979image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:05:03.342432image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:05:09.465327image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:27.305014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:33.455747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:39.953491image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:46.898957image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:53.440612image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:59.823113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:06.544066image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:13.202277image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:19.588532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:25.138674image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:31.901849image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:38.748112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:44.867641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:51.386098image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:57.736268image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:05:03.693698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:05:09.754277image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:27.631711image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:33.879223image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:40.336342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:47.254099image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:03:53.798613image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:00.198030image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:06.873584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:13.511938image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:19.887343image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:25.645709image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:32.223405image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:39.154927image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:45.219156image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:51.864641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:04:58.061534image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-15T15:05:04.109782image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-05-15T15:05:33.081275image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
GOODS_DESCRIPTION_len_chars_maxGOODS_DESCRIPTION_len_chars_meanGOODS_DESCRIPTION_len_chars_medianGOODS_DESCRIPTION_len_chars_minGOODS_DESCRIPTION_len_chars_stdGOODS_DESCRIPTION_len_chars_sumGOODS_DESCRIPTION_len_words_maxGOODS_DESCRIPTION_len_words_meanGOODS_DESCRIPTION_len_words_medianGOODS_DESCRIPTION_len_words_minGOODS_DESCRIPTION_len_words_stdGOODS_DESCRIPTION_len_words_sumHS06_countsubtokenization_indicator_maxsubtokenization_indicator_meansubtokenization_indicator_mediansubtokenization_indicator_stdsubtokenization_indicator_sum
GOODS_DESCRIPTION_len_chars_max1.0000.5090.384-0.6130.5790.8160.9490.5070.5030.2060.5810.8120.8130.5910.1510.0970.2470.800
GOODS_DESCRIPTION_len_chars_mean0.5091.0000.904-0.3060.6480.5650.5550.9500.7630.0000.7500.5560.5030.5160.5040.4370.4050.541
GOODS_DESCRIPTION_len_chars_median0.3840.9041.000-0.2390.3640.5260.4370.8870.8320.0000.4900.5200.4660.5410.5670.4550.4760.511
GOODS_DESCRIPTION_len_chars_min-0.613-0.306-0.2391.000-0.306-0.707-0.608-0.332-0.3930.760-0.326-0.709-0.711-0.544-0.204-0.163-0.251-0.697
GOODS_DESCRIPTION_len_chars_std0.5790.6480.364-0.3061.0000.3320.5800.5640.3100.0940.9180.3210.2920.2070.1830.2390.1150.304
GOODS_DESCRIPTION_len_chars_sum0.8160.5650.526-0.7070.3321.0000.8550.5820.6210.0000.4100.9990.9960.7980.3300.2080.4290.996
GOODS_DESCRIPTION_len_words_max0.9490.5550.437-0.6080.5800.8551.0000.5510.5230.0000.6080.8510.8480.6550.2330.1640.3190.845
GOODS_DESCRIPTION_len_words_mean0.5070.9500.887-0.3320.5640.5820.5511.0000.8370.0000.7220.5830.5240.5200.4420.3810.3500.557
GOODS_DESCRIPTION_len_words_median0.5030.7630.832-0.3930.3100.6210.5230.8371.0000.1360.4320.6230.5800.6080.4250.3000.4020.603
GOODS_DESCRIPTION_len_words_min0.2060.0000.0000.7600.0940.0000.0000.0000.1361.0000.2030.0000.0000.0000.0000.0000.0000.000
GOODS_DESCRIPTION_len_words_std0.5810.7500.490-0.3260.9180.4100.6080.7220.4320.2031.0000.4050.3620.3150.2430.2570.1840.385
GOODS_DESCRIPTION_len_words_sum0.8120.5560.520-0.7090.3210.9990.8510.5830.6230.0000.4051.0000.9960.7970.3210.2000.4240.995
HS06_count0.8130.5030.466-0.7110.2920.9960.8480.5240.5800.0000.3620.9961.0000.7880.2980.1790.4070.995
subtokenization_indicator_max0.5910.5160.541-0.5440.2070.7980.6550.5200.6080.0000.3150.7970.7881.0000.6280.4080.7980.816
subtokenization_indicator_mean0.1510.5040.567-0.2040.1830.3300.2330.4420.4250.0000.2430.3210.2980.6281.0000.8980.8290.372
subtokenization_indicator_median0.0970.4370.455-0.1630.2390.2080.1640.3810.3000.0000.2570.2000.1790.4080.8981.0000.5970.248
subtokenization_indicator_std0.2470.4050.476-0.2510.1150.4290.3190.3500.4020.0000.1840.4240.4070.7980.8290.5971.0000.461
subtokenization_indicator_sum0.8000.5410.511-0.6970.3040.9960.8450.5570.6030.0000.3850.9950.9950.8160.3720.2480.4611.000

Missing values

2025-05-15T15:05:10.283561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-15T15:05:11.433267image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

HS06_countGOODS_DESCRIPTION_len_words_sumGOODS_DESCRIPTION_len_words_minGOODS_DESCRIPTION_len_words_meanGOODS_DESCRIPTION_len_words_medianGOODS_DESCRIPTION_len_words_maxGOODS_DESCRIPTION_len_words_stdGOODS_DESCRIPTION_len_chars_sumGOODS_DESCRIPTION_len_chars_minGOODS_DESCRIPTION_len_chars_meanGOODS_DESCRIPTION_len_chars_medianGOODS_DESCRIPTION_len_chars_maxGOODS_DESCRIPTION_len_chars_stdsubtokenization_indicator_sumsubtokenization_indicator_minsubtokenization_indicator_meansubtokenization_indicator_mediansubtokenization_indicator_maxsubtokenization_indicator_std
HS02
0112977516.0077525.0203.8598424417434.24031030.012422.967972181.7710131.01.4090781.2857143.00.451324
02293155815.3174065.0143.0472779857433.64163832.08915.813486846.6701691.02.8896591.66666733.04.756294
036218212.9354842.091.6080841124418.12903214.55410.85747396.3515871.01.5540581.4166673.40.608181
041233684415.5506895.0203.57447243568435.33495530.09519.1387763559.7414071.02.8870571.75000036.04.596942
05153612.4000002.051.055597205513.66666714.0305.80229820.8833331.01.3922221.3333332.00.401429
068326413.1807233.081.5313361928423.22891622.05711.845886169.2285711.02.0388982.0000007.51.067709
0726481313.0795453.091.4841744913418.60984817.0698.875312419.9301591.01.5906451.5000005.50.681658
0831782112.5899052.0101.4149835250416.56151415.0427.823619601.5126981.01.8975161.6666678.00.958388
09559198113.5438283.0122.08797411397420.38819319.07210.197579957.3515871.01.7126151.5000009.00.735113
10285141314.9578954.0112.5560587969427.96140428.07313.307651454.4557001.01.5945811.5000005.50.587020
HS06_countGOODS_DESCRIPTION_len_words_sumGOODS_DESCRIPTION_len_words_minGOODS_DESCRIPTION_len_words_meanGOODS_DESCRIPTION_len_words_medianGOODS_DESCRIPTION_len_words_maxGOODS_DESCRIPTION_len_words_stdGOODS_DESCRIPTION_len_chars_sumGOODS_DESCRIPTION_len_chars_minGOODS_DESCRIPTION_len_chars_meanGOODS_DESCRIPTION_len_chars_medianGOODS_DESCRIPTION_len_chars_maxGOODS_DESCRIPTION_len_chars_stdsubtokenization_indicator_sumsubtokenization_indicator_minsubtokenization_indicator_meansubtokenization_indicator_mediansubtokenization_indicator_maxsubtokenization_indicator_std
HS02
88293114313.9010243.0142.0171617640426.07508523.08814.133942555.7289681.01.8966861.5714296.2000000.999233
899838413.9183673.0142.9833682433424.82653118.09621.109936153.9007941.01.5704161.5000004.6666670.643925
90116115331314.5915944.0282.982281364062331.35492227.015019.63581122713.1371431.01.9561741.75000016.0000001.021780
91496216214.3588714.0232.69384713770427.76209725.013417.199596783.8504251.01.5803441.4000007.0000000.722901
92317105513.3280763.0132.1182456798421.44479517.08814.485947439.1285881.01.3852641.0000004.0000000.582285
936424213.7812503.0122.6753951578424.65625020.06816.408688130.5988101.02.0406061.7500006.5000001.173693
9479213316914.1874763.0322.764096204300325.79219821.015017.27220412475.9027121.01.5750411.33333316.0000000.859632
9541911704414.0668104.0282.236884100155323.89763821.015012.9806646111.0751691.01.4581421.33333310.5000000.631161
9630911245514.0294403.0222.46084775242324.34228421.013214.9852775138.4369481.01.6623871.50000010.5000000.816961
979429613.1489362.5132.2382671972620.97872317.07413.922967139.6598291.01.4857431.0555564.0000000.721784